GITNUXSOFTWARE ADVICE
Agriculture FarmingTop 8 Best Precision Agriculture Software of 2026
Ranking roundup of top Precision Agriculture Software tools for farm data, mapping, and agronomy. Includes FarmLogs, Climate FieldView, Agworld.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
FarmLogs
Prescription-linked task tracking ties agronomic plans to executed field work.
Built for fits when farm teams need integrated precision workflows with controlled access and API automation..
Climate FieldView
Editor pickField-level agronomy data model that links prescriptions, operations, and results for automation.
Built for fits when agronomy teams need controlled field workflows with API-based integration..
Agworld
Editor pickAPI-driven workflow and agronomy data synchronization with farm and field entity mapping.
Built for fits when farm teams need API-based workflow automation with governance and auditability..
Related reading
Comparison Table
This comparison table maps precision agriculture tools across integration depth, data model design, and the automation and API surface used for ingest, analysis, and task execution. It also highlights admin and governance controls such as RBAC, audit log coverage, and provisioning workflows so organizations can assess schema fit, extensibility, and configuration patterns without guessing.
FarmLogs
field operationsTracks field and crop operations with a farm data model, work planning, and integrations that support precision agriculture workflows.
Prescription-linked task tracking ties agronomic plans to executed field work.
FarmLogs records field context, tasks, and prescription-linked activities in one place so teams can trace decisions to outcomes across seasons. The data model links agronomic inputs to field work events, which reduces rework when correcting schedules or updating treatment plans. API-driven extensibility and automation support provisioning data flows into the same schema used by the UI, instead of parallel spreadsheets. Admin controls include user access configuration and audit log coverage for changes to key records used in operations.
A practical tradeoff is that deep automation works best when integrations follow FarmLogs schema conventions and required identifiers for fields, events, and prescriptions. Teams that need high-throughput imports can hit friction when source systems require heavy normalization before ingestion. FarmLogs fits situations where a farm office or agronomy team needs consistent operational records across multiple fields and staff, with external apps syncing the same entities.
- +Schema-based linkage between prescriptions, tasks, and field activity history
- +API and automation surface for syncing operational and agronomic records
- +RBAC-style access control helps segment farm staff and admin roles
- +Audit log records changes to key operational entities
- –Integration requires schema alignment for fields, prescriptions, and events
- –High-throughput data loads need careful mapping from source identifiers
Ag operations managers
Coordinate multi-field execution against prescriptions
Fewer plan execution gaps
Integration engineers
Sync field events from external tools
Lower manual data entry
Show 2 more scenarios
Farm office admins
Control staff permissions and changes
Better governance and traceability
Apply role-based access and review audit logs for operational record edits.
Crop advisors
Maintain versioned recommendations by field
Clearer decision audit trails
Connect agronomic inputs to field activities so recommendations map to outcomes.
Best for: Fits when farm teams need integrated precision workflows with controlled access and API automation.
More related reading
Climate FieldView
geospatial platformCentralizes farm data from equipment and sensors into a geospatial field model and supports automation and integrations for prescription and task management.
Field-level agronomy data model that links prescriptions, operations, and results for automation.
Climate FieldView fits operations teams that need tight integration with existing farm management processes and consistent field-level identifiers across seasons. The data model maps agronomic entities like fields, hybrid or variety selections, applications, and results into records that can be referenced by automations. Automation and extensibility depend on a documented API surface plus configuration and provisioning patterns for connecting data sources. Governance is handled through admin controls that separate account access by role and track system activity through audit logging where enabled.
A tradeoff appears in how deeply teams must align equipment and mapping standards to maintain schema consistency across imports. Climate FieldView works best when farm datasets are already standardized, such as when vehicle telemetry, prescription layers, and scouting notes share field boundaries and consistent product codes. In situations where field boundaries or product codes change frequently, automation throughput can drop because reconciliation becomes manual.
- +API-first integration for field, application, and yield records
- +Configurable workflow patterns tied to agronomy entities
- +Role-based access controls with admin account separation
- +Audit logs support traceability of configuration and data actions
- –Schema consistency depends on standardized field boundaries and codes
- –Automation requires careful provisioning of connected data sources
- –Reconciliation effort increases when imports conflict with existing records
Farm ops managers
Standardize tasks across fields and operators
Fewer data mismatches
Ag retail integration engineers
Sync prescriptions with downstream systems
Faster reporting ingestion
Show 2 more scenarios
Data governance leads
Enforce RBAC and trace changes
Stronger access control
Apply role-based access and review audit logs for configuration and data modification events.
Agronomists
Coordinate scouting and application decisions
More consistent decisions
Tie scouting observations to field identifiers so follow-on operations reference consistent records.
Best for: Fits when agronomy teams need controlled field workflows with API-based integration.
Agworld
farm record systemStores agronomy records and field activities in a configurable schema and supports collaboration, automation, and export workflows.
API-driven workflow and agronomy data synchronization with farm and field entity mapping.
Agworld focuses on integration depth by connecting farm and field objects to agronomy artifacts such as tasks, observations, and reports. The data model is built around agronomic workflows that can be parameterized per farm unit and mapped into external systems through API-driven synchronization. Automation is centered on provisioning and configuration of workflow elements so recurring operations follow the same structure across seasons. Extensibility is strongest where workflows and reference data need to round-trip with other enterprise systems.
A key tradeoff is that automation and customization require alignment with Agworld’s workflow schema rather than free-form record design. Teams that need fully bespoke data structures across every sensor type may find the schema constraints limit throughput for edge cases. Agworld fits teams running repeatable agronomy programs across multiple farms where controlled configuration, RBAC, and audit log visibility matter for governance.
- +Workflow data model links fields, tasks, and agronomy outputs
- +API-first integration supports schema-driven synchronization
- +RBAC and audit logs improve change traceability and provisioning control
- +Automation favors repeatable operations configured per farm unit
- –Schema alignment limits highly bespoke data modeling
- –Automation complexity increases when workflows vary by site
Farm operations managers
Standardize seasonal agronomy task execution
Consistent operations and reporting
Agronomy data teams
Sync observations into data warehouses
Faster analytics refresh cycles
Show 2 more scenarios
Systems integrators
Connect Agworld with ERP and CRM
Reduced manual data re-entry
Provision farm and work entities through API calls and maintain controlled data exchange.
Operations governance teams
Audit changes to agronomy workflows
Improved operational compliance
Rely on RBAC controls and audit logs to trace provisioning and configuration updates.
Best for: Fits when farm teams need API-based workflow automation with governance and auditability.
OneSoil
imagery analyticsCollects field imagery and agronomic signals into a spatial data model and exposes workflow APIs and exports for precision agriculture decisions.
Governed workflow provisioning that links agronomic tasks to normalized field and crop data.
OneSoil is a precision agriculture software focused on agronomic workflow execution and field data orchestration. Its distinct angle comes from how it ties crop inputs to a governed data model and repeatable automation runs.
The core capabilities center on managing field activities, ingesting and normalizing agronomic data, and routing tasks to people and systems through configurable workflows. Automation and API access support integration breadth while admin controls control who can change schemas, rules, and provisioning.
- +Configurable agronomic workflow automation tied to a structured data model
- +API and integration points for ingesting field data and triggering actions
- +Task provisioning supports role-based execution across field and operations teams
- +Administration controls support configuration management and change governance
- –Integration depth depends on supported data sources and mapping requirements
- –Workflow schema changes can require careful coordination across teams
- –Automation throughput can be constrained by workflow complexity and run volume
Best for: Fits when mid-size farm operations need governed workflow automation and API-based integrations.
Farmbot
automation frameworkProvides device-centric farm automation with a programmable data model and APIs for managing precision planting and sensor-driven tasks.
Open API plus coordinate schema for bed, crop, and route execution across the farm state.
Farmbot automates field tasks by pairing a farmbot controller with device-aware software that executes recorded routes and routines. Farmbot’s data model centers on crops, beds, coordinates, and device objects so configuration changes map directly to physical actions.
A documented API enables provisioning, command execution, and extensibility through integrations that read and write farm state. Automation runs through schedules, task definitions, and event-triggered flows tied to the same coordinate schema used for planning.
- +Coordinate-based data model links beds, crops, and actions to physical locations
- +Documented API supports provisioning, state reads, and command execution
- +Task automation ties schedules to the same objects used for route planning
- +Extensibility via integrations that persist farm state and controller actions
- –Automation depth depends on accurate calibration and coordinate consistency
- –Complex governance needs separate operational processes since RBAC details are limited
- –Higher throughput workflows require careful rate control on automation calls
- –Schema changes can require coordinated updates across controller and external tools
Best for: Fits when teams need coordinate-driven automation and a stable API for farm control.
Agrivi
task managementTracks farm tasks and crop calendars with a configurable data model and automation for record keeping across precision activities.
Farm operations workflow ties task scheduling to agronomy records through a structured data model.
Agrivi fits teams that run farm operations across multiple fields and need operational planning tied to crop activities. The system centers on a farm data model for tasks, operations, and inputs, then links schedules to field workflows.
Agrivi supports integration through published interfaces for data exchange and automation hooks around agronomy activities and reporting. Admin governance focuses on role-based access, workspace permissions, and activity tracking for operational changes.
- +Field and crop schema links operations, tasks, and input records
- +Workflow automation connects planned activities to execution states
- +Integration surface supports API-based data exchange for operational records
- +Role-based access controls separate operational users from admin roles
- +Audit-style activity history helps track changes to plans and operations
- –Complex integrations require careful mapping to Agrivi data schema
- –Automation boundaries depend on available API endpoints and event hooks
- –Bulk provisioning workflows need extra process for large multi-farm setups
- –Reporting depth can require repeated configuration per crop and operation type
Best for: Fits when multi-field teams need scheduled agronomy workflows with controlled access.
SmartFarmingSystems
IoT data layerConnects farm IoT and field data into a managed data layer with automation workflows for precision agriculture operations.
Schema-driven ingestion plus RBAC and audit logging for governed automation workflows.
SmartFarmingSystems focuses on integration depth for precision agriculture workflows, with an explicit data model for field, crop, and operational records. The system supports automation through configurable rule-based tasks and schema-driven data ingestion, reducing manual data reconciliation across devices and vendors.
Admin controls emphasize RBAC, provisioning, and audit logging for changes to configuration and permissions. Extensibility is shaped around an API surface meant for event ingestion, workflow triggers, and controlled data throughput.
- +Documented data model for fields, crops, and operations reduces schema drift.
- +RBAC plus provisioning supports controlled access across farms and operators.
- +Automation rules can trigger on ingested events and operational state changes.
- +API supports event ingestion and workflow triggers for external systems.
- +Audit logs track configuration and permission changes for governance.
- –Automation coverage depends on available event types and defined schema mappings.
- –Complex multi-vendor integrations may require significant schema mapping work.
- –Higher governance needs can add admin overhead for role design and approvals.
Best for: Fits when mid-size teams need controlled automation and a documented API data model.
AgOpenGPS
guidance automationImplements open guidance logic for precision steering with configuration files that define automation behavior for field operations.
API-backed mission and guidance configuration exchange for integrating external apps.
AgOpenGPS is precision agriculture software focused on vehicle guidance and field operations planning, with configuration patterns that fit low-cost hardware deployments. Integration depth centers on how well mission files, guidance settings, and operational tasks map into a consistent data model for repeatable runs.
Automation and extensibility rely on configurable workflows and a documented integration path through its API and developer-facing surfaces. Admin and governance controls are comparatively limited, with fewer enterprise-grade RBAC and audit log mechanisms than general farm management suites.
- +Guidance workflow is driven by configuration files tied to repeatable field runs
- +API and automation surface supports external tooling for mission and settings exchange
- +Data model stays consistent across guidance, task definitions, and run outputs
- +Extensibility focuses on integrating external apps into existing guidance pipelines
- –RBAC granularity and role-based provisioning are limited for multi-user operations
- –Audit log depth is thinner than broader farm management systems
- –Automation throughput can be constrained by manual configuration for complex scenarios
- –Governance controls lack enterprise-style policy enforcement and approvals
Best for: Fits when teams need configurable guidance automation with integration to external tools.
How to Choose the Right Precision Agriculture Software
This buyer's guide covers FarmLogs, Climate FieldView, Agworld, OneSoil, Farmbot, Agrivi, SmartFarmingSystems, and AgOpenGPS for precision agriculture workflows that combine field planning, agronomic records, and automation.
The guide focuses on integration depth, data model design, automation and API surface, and admin and governance controls so teams can map field and prescription inputs to executed work with traceability.
Precision agriculture software that models fields, prescriptions, and executed work
Precision agriculture software uses an agronomy and operations data model to connect field boundaries, crop or input plans, and task execution into a traceable workflow. It solves problems such as syncing field and agronomic records from equipment and operators, routing tasks to people or systems, and reconciling planning records with executed events.
FarmLogs shows how prescription-linked task tracking ties agronomic plans to executed field work in a schema-based workflow, while Climate FieldView shows how a field-level agronomy data model links prescriptions, operations, and results for automation.
Evaluation criteria for integration, automation, and governed execution
Integration depth matters because teams must align field identifiers, prescriptions, and events across equipment, scouting, and farm management systems. Tools like FarmLogs and Climate FieldView center their automation around a structured agronomy data model so integrations have consistent targets for sync.
Automation and API surface matter because provisioning, workflow triggering, and event ingestion determine throughput during the season. Governance controls matter because multi-user farms need RBAC, audit log traceability, and change control over schemas and operational workflows, as shown in FarmLogs, Climate FieldView, Agworld, OneSoil, and SmartFarmingSystems.
Prescription-linked task tracking tied to field activity history
FarmLogs links prescriptions to executed work through task tracking connected to field activity history, which reduces gaps between agronomic intent and operational reality. Climate FieldView also ties field workflows to prescriptions and execution so automation can pivot off agronomy entity states.
Field and crop data model that links operations to outcomes
Climate FieldView provides a field-level agronomy data model that links prescriptions, operations, and results for automation. OneSoil and Agrivi both emphasize structured data models that normalize field and crop inputs so workflow steps attach to consistent agronomic entities.
API-first integration for schema-driven synchronization
FarmLogs, Climate FieldView, and Agworld all emphasize API and automation surfaces designed for pushing and syncing operational and agronomic records between systems. Agworld focuses on API-driven workflow and agronomy data synchronization using farm and field entity mapping, which matters when multiple systems produce partial records.
Workflow automation tied to configurable agronomy entities
Climate FieldView and Agrivi support configurable workflow patterns tied to agronomy entities and field operations, which helps teams automate task organization around scheduled work. OneSoil focuses on configurable agronomic workflow automation tied to a structured data model and repeatable automation runs.
RBAC-style access control and audit logs for operational governance
FarmLogs offers RBAC-style access control plus an audit log that records changes to key operational entities, which supports controlled collaboration between farm staff and admin roles. Climate FieldView, Agworld, and SmartFarmingSystems also include role-based access control with admin separation and audit logs that support traceability of configuration and permission changes.
Provisioning and governed control over schemas, rules, and mappings
OneSoil’s governed workflow provisioning links agronomic tasks to normalized field and crop data, which helps prevent uncontrolled schema drift across workflow teams. SmartFarmingSystems emphasizes provisioning plus schema-driven ingestion so teams can manage ingestion and rule execution with governed configuration and controlled throughput.
Choose based on data model alignment and automation governance
A practical selection starts with the integration targets and the identifiers that must stay stable across systems. FarmLogs and Climate FieldView both depend on schema and field boundary consistency, so mapping field and prescription identifiers early prevents downstream reconciliation churn.
Next evaluate the automation surface and the operational controls that govern who can change workflows and schemas. OneSoil, SmartFarmingSystems, Agworld, and FarmLogs provide RBAC and audit log traceability that supports controlled provisioning, while Farmbot and AgOpenGPS prioritize coordinate and configuration exchange with comparatively limited enterprise governance.
Map the agronomy objects and decide which data model drives automation
List the objects that must connect end to end, such as fields, prescriptions, operations, inputs, and executed events, then test whether FarmLogs, Climate FieldView, or Agworld can model those objects directly. If the main linkage is prescription to execution, FarmLogs fits because it ties prescriptions to task tracking connected to field activity history.
Confirm API surfaces for sync direction and event ingestion
Verify whether the tool supports pushing or syncing operational and agronomic records, then confirm whether it exposes APIs for workflow triggering and event ingestion. FarmLogs and Agworld emphasize schema-driven synchronization via API and automation, while SmartFarmingSystems supports event ingestion and workflow triggers designed for external systems.
Plan for schema alignment and identifier mapping before onboarding
Treat schema alignment as a provisioning task because FarmLogs requires careful mapping from source identifiers for high-throughput loads and precision workflows. Climate FieldView and Agrivi also require consistent field boundaries and codes, so imports conflict with existing records when mappings differ.
Set governance expectations for RBAC, audit logs, and configuration change control
For multi-user farms, prioritize tools with explicit RBAC and audit log traceability tied to operational entities and configuration actions. FarmLogs, Climate FieldView, Agworld, and SmartFarmingSystems support RBAC-style access control plus audit logs that track changes to permissions and configuration.
Validate automation throughput constraints with workflow complexity
Estimate automation volume and workflow complexity and then check whether the tool’s automation can sustain it without manual coordination gaps. OneSoil highlights that automation throughput can be constrained by workflow complexity and run volume, while Farmbot notes that higher-throughput automation calls require careful rate control.
Match tool scope to operations type: agronomy workflows versus guidance control
Choose agronomy and operations workflow suites for task scheduling, prescriptions, and record governance such as Climate FieldView, Agworld, FarmLogs, and Agrivi. Choose guidance-focused or device-focused systems such as AgOpenGPS and Farmbot when the primary requirement is configuration exchange for mission and route execution tied to coordinate or guidance settings.
Which teams benefit from governed precision agriculture workflow software
Precision agriculture workflow software benefits teams that need structured linkage between agronomic intent and executed work across multiple users, assets, and data sources. The best fit depends on whether automation is driven by prescriptions and field operations, by event ingestion and governed workflow triggers, or by coordinate-based guidance and device routines.
Farm and agronomy teams that need API-driven integrations and traceable collaboration should focus on tools that pair structured data models with RBAC and audit logs, while automation for guidance or controllers fits tools with coordinate or configuration exchange.
Operations and agronomy teams needing prescription-to-execution traceability
FarmLogs fits this segment because prescription-linked task tracking ties agronomic plans to executed field work and connects those executions to field activity history. Climate FieldView also fits because it links prescriptions, operations, and results using a field-level agronomy data model for automation.
Teams building API-driven agronomy workflow integrations
Agworld fits because it emphasizes API-first integration for schema-driven synchronization and workflow and agronomy data synchronization with farm and field entity mapping. FarmLogs and Climate FieldView also fit because their automation surfaces support syncing operational and agronomic records through API access.
Multi-site teams requiring governed workflow provisioning and change traceability
OneSoil fits because it uses governed workflow provisioning tied to normalized field and crop data and supports admin controls for configuration management and change governance. SmartFarmingSystems fits because it pairs schema-driven ingestion with RBAC, provisioning, and audit logging for governed automation workflows.
Multi-field farms that schedule operations and track task execution status
Agrivi fits because it ties farm operations workflow and task scheduling to agronomy records through a structured data model and connects schedules to field workflows. Agrivi also fits because its audit-style activity history helps track changes to plans and operations under role-based access controls.
Teams prioritizing guidance logic, coordinate execution, and controller-style automation
Farmbot fits because its coordinate-based data model links beds, crops, and actions to physical locations and supports a documented API for provisioning and command execution. AgOpenGPS fits because mission and guidance configuration exchange is driven by configuration files tied to repeatable field runs and supported through an API for external tooling.
Pitfalls that break precision agriculture integrations and governance
Many precision agriculture implementations stall because field identifiers, prescription records, and event mappings drift between systems. Tool-specific limitations also affect automation throughput and governance depth when workflows become complex or multi-user governance requirements increase.
The concrete failures below come from schema alignment challenges, provisioning complexity, and mismatched expectations around RBAC and audit log depth across tools like FarmLogs, Climate FieldView, OneSoil, Farmbot, and AgOpenGPS.
Treating schema alignment as a minor import step
FarmLogs, Climate FieldView, and Agrivi all depend on consistent field and identifier mapping, so unplanned schema alignment work creates reconciliation effort when imports conflict with existing records. Start with a mapping plan for field boundaries, prescriptions, and events before loading historical data into FarmLogs or Climate FieldView.
Overestimating how much automation runs can sustain without workflow governance
OneSoil and Farmbot both flag throughput constraints tied to workflow complexity and automation run volume, so unbounded task run generation can slow automation calls. Use workflow configuration controls in OneSoil and apply rate control expectations when integrating with Farmbot controller commands.
Choosing shallow governance when multiple roles must co-author operational changes
AgOpenGPS provides thinner audit log depth and limited RBAC granularity compared with farm management suites, which can create governance gaps when many users update missions and settings. If role-based provisioning and audit traceability are core requirements, prioritize FarmLogs, Climate FieldView, Agworld, OneSoil, or SmartFarmingSystems.
Picking a guidance-first tool for agronomy workflow orchestration
AgOpenGPS and Farmbot focus on mission and coordinate execution, so they can leave agronomy prescription linkage and task scheduling gaps when the primary requirement is prescription-linked work planning. Use Climate FieldView, FarmLogs, or Agworld when the center of gravity is field prescriptions, tasks, and executed agronomy records.
How We Selected and Ranked These Tools
We evaluated FarmLogs, Climate FieldView, Agworld, OneSoil, Farmbot, Agrivi, SmartFarmingSystems, and AgOpenGPS using criteria that weigh features most heavily, then ease of use, then value. Each tool received a features rating, an ease of use rating, and a value rating, and the overall rating acted as a weighted average where features carry the most weight and ease of use and value each account for the rest. This is criteria-based editorial scoring grounded in the provided capability descriptions for data model structure, integration and API surface, automation behavior, and governance controls.
FarmLogs set itself apart by combining schema-based precision workflow modeling with prescription-linked task tracking tied to executed field work, plus audit log and RBAC-style access control that supports multi-user governance. That combination lifted FarmLogs on the features factor through traceable prescription-to-execution linkage and on ease-of-use and value through structured workflow planning rather than manual reconciliation.
Frequently Asked Questions About Precision Agriculture Software
Which precision agriculture platforms provide prescription-linked task tracking tied to field execution?
How do FarmLogs, Climate FieldView, and Agworld differ in their agronomy data model and workflow execution approach?
What integration and API patterns matter most for syncing field data between devices and external systems?
Which tools support governance controls like RBAC, audit logs, and permissioning for multi-user farm operations?
How should teams handle data migration when moving existing field records into a new precision agriculture software system?
What admin controls exist for provisioning workflows and controlling configuration changes?
Which platforms are best suited for coordinate-driven automation tied to physical bed or route execution?
Which tools support extensibility for event ingestion and workflow-trigger automation with controlled throughput?
How do OneSoil and Agworld differ when the workflow depends on governed crop inputs linked to execution?
Conclusion
After evaluating 8 agriculture farming, FarmLogs stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Keep exploring
Comparing two specific tools?
Software Alternatives
See head-to-head software comparisons with feature breakdowns, pricing, and our recommendation for each use case.
Explore software alternatives→In this category
Agriculture Farming alternatives
See side-by-side comparisons of agriculture farming tools and pick the right one for your stack.
Compare agriculture farming tools→FOR SOFTWARE VENDORS
Not on this list? Let’s fix that.
Our best-of pages are how many teams discover and compare tools in this space. If you think your product belongs in this lineup, we’d like to hear from you—we’ll walk you through fit and what an editorial entry looks like.
Apply for a ListingWHAT THIS INCLUDES
Where buyers compare
Readers come to these pages to shortlist software—your product shows up in that moment, not in a random sidebar.
Editorial write-up
We describe your product in our own words and check the facts before anything goes live.
On-page brand presence
You appear in the roundup the same way as other tools we cover: name, positioning, and a clear next step for readers who want to learn more.
Kept up to date
We refresh lists on a regular rhythm so the category page stays useful as products and pricing change.